Startup Idea Validation

Systematic validation for testing ideas before building: define hypotheses, collect evidence, score the opportunity, and make a decision you can defend.

Operating Principles (2026)

• Prefer decisions over inventories: each dimension ends with GO / CONDITIONAL / PIVOT / NO-GO and a next action.

• Separate evidence quality from confidence: weak evidence cannot justify a high score.

• Pre-register thresholds and stop rules before running experiments (avoid moving goalposts).

• Validate willingness-to-pay and time-to-value early (price is part of the product).

• Calibrate thresholds to the target outcome (venture-scale vs cash-flow business) and business model (B2B SaaS, B2C, marketplace, services).

• Stay safe and ethical: no misrepresentation, respect ToS, and handle customer data with minimization and retention limits.

Intake Checklist (Ask First)

• One-sentence idea + target user + job-to-be-done

• Business model: B2B/B2C, SaaS/usage-based/marketplace/services, ACV/ARPU range

• Geography, constraints (regulated domain, procurement/security requirements, data access)

• Target outcome: venture-scale, profitable small business, or thesis-driven R&D

• Current evidence: interviews, pilots, pre-sales, traffic, competitor list, pricing assumptions

Choose the Right Output

If the user asks… Produce… Use…

“Validate this idea” / “Is this worth building?” 9-dimension scorecard + verdict validation-scorecard.md, go-no-go-decision.md

“What’s the riskiest assumption?” RAT + test plan riskiest-assumption-test.md, validation-experiment-planner.md

“Test my hypothesis” Hypothesis canvas + experiment design hypothesis-canvas.md, hypothesis-testing-guide.md

“Market size for X” TAM/SAM/SOM sizing + assumptions table market-sizing-worksheet.md, market-sizing-patterns.md

“Can this be profitable / what’s my runway?” Unit economics + runway + scenarios financial-modeling-calculator.md

“Should I build X or Y?” Comparative scorecard + decision memo validation-scorecard.md, go-no-go-decision.md

Workflow

• Clarify the target outcome and business model; set default thresholds accordingly.

• Identify the RAT (the assumption that kills the business if wrong).

• Plan the validation ladder: interviews -> smoke test -> concierge/WoZ -> paid pilot.

• Run the cheapest falsifiable test first; pre-register PASS/FAIL thresholds and stop rules.

• Score all 9 dimensions using evidence; downgrade scores when evidence is weak.

• Produce a decision memo: verdict, why, what would change the decision, and the next smallest reversible step.

9-Dimension Scorecard

Dimension Weight What it measures

Problem severity 15% Urgency, cost of inaction, current workarounds

Market size 12% Sufficient demand for the target outcome

Market timing 10% Clear “why now” and tailwinds

Competitive moat 12% Defensibility over time

Unit economics 15% Profit path (incl. payback and margins)

Founder-market fit 8% Access, expertise, and execution capability

Technical feasibility 10% Buildability, dependencies, constraints

GTM clarity 10% ICP, channels, motion, first customers

Risk profile 8% What can kill it and likelihood

Verdict thresholds (default):

• 80–100 : GO

• 60–79 : CONDITIONAL (validate RAT first)

• 40–59 : PIVOT

• <40 : NO-GO

Deep scoring rubrics and calibration live in validation-methodology.md.

Evidence Rules

• Strong evidence is behavioral commitment with cost (time, money, switching, access); weak evidence is opinions and hypotheticals.

• Triangulate important claims across at least two sources (especially market sizing and competitor state).

• Keep an evidence trail: link + capture month; separate “fact” vs “assumption”.

Validation Ladder (Default)

Step Goal Strong signal

Interviews Validate the problem and context Repeated pain with real workarounds and spend

Smoke test Validate demand Qualified conversion with price shown

Concierge/WoZ Validate workflow value Users complete the job and return

Paid pilot Validate willingness-to-pay Paid, renewed, or expanded

AI / Automation Notes (2026)

If the idea depends on AI (agents, copilots, automation), validate these explicitly:

• Data rights and access: can you legally and reliably access required data?

• Reliability: define success metrics, failure modes, and human fallback; validate on real workflows.

• Cost-to-serve: model inference + retrieval + human-in-the-loop costs in assets/financial-modeling-calculator.md .

See hypothesis-testing-guide.md for AI-specific experiment patterns.

Integration Points

Receives From

• startup-review-mining - Pain point evidence

• startup-trend-prediction - Market timing inputs

• startup-competitive-analysis - Competitor landscape

Feeds Into

• router-startup - Startup decision routing

• product-management - Validated requirements and roadmap inputs

• startup-business-models - Monetization and packaging decisions

Resources

Resource Purpose

validation-methodology.md Scoring rubrics and calibration

hypothesis-testing-guide.md Experiment design and RAT workflows

market-sizing-patterns.md TAM/SAM/SOM methods and pitfalls

moat-assessment-framework.md Defensibility analysis

Templates

Template Purpose

validation-scorecard.md Full 9-dimension scoring

go-no-go-decision.md Decision memo format

hypothesis-canvas.md Hypothesis definition

validation-experiment-planner.md Experiment planning + thresholds

riskiest-assumption-test.md RAT identification and test design

market-sizing-worksheet.md Sizing worksheet

financial-modeling-calculator.md Runway + scenarios + unit economics

Data

File Purpose

sources.json Curated validation resources